{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf9a11e5",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3410558f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f1c6219dc6e4d7da6503b16d171cbce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/16 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最近 2 年上涨月数/总月数：11/24 | 概率：45.83%\n",
      "最近 3 年上涨月数/总月数：18/36 | 概率：50.00%\n",
      "最近 5 年上涨月数/总月数：32/60 | 概率：53.33%\n",
      "最近 8 年上涨月数/总月数：46/96 | 概率：47.92%\n",
      "最近 10 年上涨月数/总月数：58/120 | 概率：48.33%\n",
      "最近 15 年上涨月数/总月数：71/136 | 概率：52.21%\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# ========== 参数设置 ==========\n",
    "symbol = symbol_code = \"电网设备\"\n",
    "year_spans = [2, 3, 5, 8, 10, 15]  # 不同周期\n",
    "\n",
    "# ========== 获取完整数据 ==========\n",
    "today = datetime.datetime.today()\n",
    "full_start_date = (today - pd.DateOffset(years=max(year_spans))).strftime('%Y%m%d')\n",
    "end_date = today.strftime('%Y%m%d')\n",
    "\n",
    "df = ak.stock_board_industry_index_ths(symbol=symbol_code, start_date=full_start_date, end_date=end_date)\n",
    "df[\"日期\"] = pd.to_datetime(df[\"日期\"])\n",
    "df = df.sort_values(\"日期\")\n",
    "\n",
    "# ========== 初始化图表数据容器 ==========\n",
    "bar_data = {}  # key: N_YEARS, value: 月份 -> 上涨次数\n",
    "\n",
    "# ========== 收集每个周期的数据 ==========\n",
    "for N_YEARS in year_spans:\n",
    "    start_cut = today - pd.DateOffset(years=N_YEARS)\n",
    "    df_cut = df[df[\"日期\"] >= start_cut].copy()\n",
    "\n",
    "    # 月份字段\n",
    "    df_cut[\"月份\"] = df_cut[\"日期\"].dt.month\n",
    "\n",
    "    # 每月开盘收盘\n",
    "    monthly_df = df_cut.groupby(df_cut[\"日期\"].dt.to_period(\"M\")).agg({\n",
    "        \"开盘价\": \"first\",\n",
    "        \"收盘价\": \"last\"\n",
    "    }).reset_index()\n",
    "\n",
    "    monthly_df[\"月份\"] = monthly_df[\"日期\"].dt.month\n",
    "    monthly_df[\"上涨\"] = monthly_df[\"收盘价\"] > monthly_df[\"开盘价\"]\n",
    "\n",
    "    up_counts = monthly_df.groupby(\"月份\")[\"上涨\"].sum()\n",
    "    total_counts = monthly_df.groupby(\"月份\")[\"上涨\"].count()\n",
    "    up_probs = up_counts / total_counts\n",
    "\n",
    "    total_up_count = up_counts.sum()\n",
    "    total_months = len(monthly_df)\n",
    "    overall_up_probability = total_up_count / total_months if total_months else 0\n",
    "\n",
    "    print(f\"最近 {N_YEARS} 年上涨月数/总月数：{total_up_count}/{total_months} | 概率：{overall_up_probability:.2%}\")\n",
    "\n",
    "    bar_data[N_YEARS] = up_counts\n",
    "\n",
    "# ========== 合成一个图表：分组柱状图 ==========\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "bar_width = 0.12\n",
    "months = np.arange(1, 13)\n",
    "offsets = np.linspace(-bar_width * len(year_spans) / 2, bar_width * len(year_spans) / 2, len(year_spans))\n",
    "\n",
    "for i, N_YEARS in enumerate(year_spans):\n",
    "    counts = bar_data.get(N_YEARS, pd.Series(0, index=range(1, 13)))\n",
    "    # 确保每月都有值\n",
    "    counts = counts.reindex(range(1, 13), fill_value=0)\n",
    "    plt.bar(months + offsets[i], counts.values, width=bar_width, label=f\"{N_YEARS}年\")\n",
    "\n",
    "plt.xticks(months, [f\"{m}月\" for m in months], rotation=-90)\n",
    "plt.xlabel(\"月份\")\n",
    "plt.ylabel(\"上涨次数\")\n",
    "plt.title(f\"{symbol}行业各周期每月上涨次数比较\")\n",
    "plt.legend(title=\"周期长度\")\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.6)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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